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Associations between cerebral amyloid and changes in cognitive function and falls risk in subcortical… Dao, Elizabeth; Best, John R; Hsiung, Ging-Yuek R; Sossi, Vesna; Jacova, Claudia; Tam, Roger; Liu-Ambrose, Teresa Jun 28, 2017

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RESEARCH ARTICLE Open AccessAssociations between cerebral amyloid andchanges in cognitive function and falls riskin subcortical ischemic vascular cognitiveimpairmentElizabeth Dao1,2, John R. Best1,2, Ging-Yuek Robin Hsiung2,3, Vesna Sossi4, Claudia Jacova5, Roger Tam6and Teresa Liu-Ambrose1,2,7*AbstractBackground: To determine the association between amyloid-beta (Aβ) plaque deposition and changes in globalcognition, executive functions, information processing speed, and falls risk over a 12-month period in older adults witha primary clinical diagnosis of subcortical ischemic vascular cognitive impairment (SIVCI).Methods: This is a secondary analysis of data acquired from a subset of participants (N = 22) who were enrolled in arandomized controlled trial of aerobic exercise (NCT01027858). The subset of individuals completed an 11C Pittsburghcompound B (PIB) scan. Cognitive function and falls risk were assessed at baseline, 6-months, and 12-months. Globalcognition, executive functions, and information processing speed were measured using: 1) ADAS-Cog; 2) Trail MakingTest; 3) Digit Span Test; 4) Stroop Test, and 5) Digit Symbol Substitution Test. Falls risk was measured using thePhysiological Profile Assessment. Hierarchical multiple linear regression analyses determined the unique contribution ofAβ on changes in cognitive function and falls risk at 12-months after controlling for experimental group (i.e. aerobicexercise training or usual care control) and baseline performance. To correct for multiple comparisons, we applied theBenjamini-Hochberg procedure to obtain a false discovery rate corrected threshold using alpha = 0.05.Results: Higher PIB retention was significantly associated with greater decrements in set shifting (Trail Making Test,adjusted R2 = 35.3%, p = 0.002), attention and conflict resolution (Stroop Test, adjusted R2 = 33.4%, p = 0.01), andinformation processing speed (Digit Symbol Substitution Test, adjusted R2 = 24.4%, p = 0.001) over a 12-month period.Additionally, higher PIB retention was significantly associated with increased falls risk (Physiological Profile Assessment,adjusted R2 = 49.1%, p = 0.04). PIB retention was not significantly associated with change in ADAS-Cog and Verbal DigitSpan Test (p > 0.05).Conclusions: Symptoms associated with SIVCI may be amplified by secondary Aβ pathology.Trial registration: ClinicalTrials.gov, NCT01027858, December 7, 2009.Keywords: Vascular cognitive impairment, Alzheimer’s disease, Amyloid, Cognitive impairment, Executivefunctions, Falls risk* Correspondence: teresa.ambrose@ubc.ca1Department of Physical Therapy, University of British Columbia, 212 – 2177Wesbrook Mall, Vancouver, BC V6T 1Z3, Canada2Djavad Mowafaghian Centre for Brain Health, University of British Columbia,2215 Wesbrook Mall, Vancouver, BC V6S 0A9, CanadaFull list of author information is available at the end of the article© The Author(s). 2017 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, andreproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link tothe Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver(http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.Dao et al. BMC Geriatrics  (2017) 17:133 DOI 10.1186/s12877-017-0522-4BackgroundAlzheimer’s disease (AD) and subcortical ischemic vas-cular cognitive impairment (SIVCI) are the two mostcommon causes of cognitive dysfunction [1], but peopleoften present with mixed pathology [2]. Pathologicalhallmarks of AD include the presence of amyloid-beta(Aβ) plaques and neurofibrillary tangles (NFT) [3]. Onthe other hand, SIVCI is characterized by the presenceof white matter hyperintensities (WMH) and lacunes. Inthe last decade, there is growing recognition of the highprevalence of mixed presentations [1, 2, 4] and manystudies have begun to investigate the impact of cerebro-vascular pathology in AD [5–7]. However, few studieshave considered mixed pathology from a primary SIVCIdiagnosis perspective.It is important to consider AD pathology within anSIVCI diagnosis as both share common pathogenicmechanisms – studies indicate a positive feedback loopeffect between Aβ and cerebrovascular dysfunction. Forexample, Aβ may cause vascular dysregulation by com-promising cerebral perfusion, reducing vascular reserves,and increasing the propensity for ischemic damage. In re-turn, hypoxia and/or ischemia may promote the produc-tion of the Aβ peptide resulting in greater Aβ plaqueaccumulation [8]. Molecular studies indicate a close inter-action between AD and SIVCI pathology [8, 9]; yet, fewstudies have been conducted to investigate the associationbetween Aβ and cognitive function in SIVCI [10–12].Furthermore, elevated levels of Aβ have been associ-ated with cognitive dysfunction. Increased Aβ plaque de-position, identified by positron emission tomography(PET), was associated with decreased episodic memoryperformance and global cognitive function in healthyolder adults, mild cognitive impairment (MCI), and ADparticipants [13, 14]. Though executive dysfunction is acharacteristic of early AD [15], few studies have assessedthe potential impact of Aβ on executive functions. Onepublished study found no association between high Aβdeposition and executive functions in MCI; however, theassessment of executive functions was limited to a com-posite of inhibition and verbal fluency [16]. Anotherstudy found that baseline global Aβ deposition in frontal,parietal, and medial temporal cortices was associatedwith greater decreases in executive functions, language,attention, information processing speed, and visuospatialfunction at 2-year follow-up in people with MCI [17].Furthermore, several recent studies have reported anassociation between amyloid and decreased mobility.Specifically, increased amyloid was associated with de-creased gait speed in cognitively normal and mildly im-paired older adults [18–20]. One study assessing specificgait parameters reported slower gait speed, lower ca-dence, longer double support time, and greater stancetime variability in older adults with high amyloid [20].Of particular relevance to our study, a 12-month pro-spective study found that higher Aβ deposition wasassociated with a faster time to first fall in community-dwelling healthy older adults [21]. These results indicatethat Aβ may have an effect on both cognitive and mobilityoutcomes.Although previous studies have begun to evaluate theimpact of Aβ on cognitive and physical function inhealthy older adults, MCI, and AD participants [13, 14],such studies in people with SIVCI are more limited[11, 12, 22, 23]. Furthermore, previous studies lack acomprehensive assessment of executive functions, infor-mation processing speed, and to our knowledge, no stud-ies have assessed falls risk [11, 22, 23]. Also, much ofcurrent knowledge is based on cross-sectional studies andfew studies have assessed the association of cerebral Aβon changes in cognitive and mobility outcomes. To ad-dress these knowledge gaps, we conducted a secondaryanalysis to assess the association of Aβ with changes incognitive function (i.e., global cognition, executive func-tions, and information processing speed) and falls riskover a 12-month period. We hypothesized that elevatedAβ plaque deposition would be associated with larger dec-rements in these measures over a 12-month period.MethodsEthical approval was obtained from the VancouverCoastal Health Research Institute (V07–01160) and theUniversity of British Columbia’s Clinical Research EthicsBoard (H07–01160). All subjects gave written informedconsent in accordance with the Declaration of Helsinki.Participants and study designThis was a planned secondary analysis of data acquiredfrom a proof-of-concept randomized controlled trial (RCT)of aerobic exercise in people with SIVCI (NCT01027858)[24]. Briefly, participants were randomized to either a 6-month thrice-weekly aerobic exercise training group or ausual care control group. Participants were followed for anadditional 6-months after completing the 6-month inter-vention period. Assessments for cognitive functions andfalls risk were performed at baseline, 6-month, and 12-month time points. To maximize our ability to detectchanges in cognitive function and falls risk, we used base-line and 12-month data for our analyses.Participants were recruited from the University of BritishColumbia Hospital Clinic for AD and Related Disorders,the Vancouver General Hospital Stroke Prevention Clinic,and specialized geriatric clinics in Metro Vancouver, BritishColumbia. The diagnosis of SIVCI was confirmed in eachparticipant by a neurologist based on the presence of cere-bral small vessel disease and cognitive impairment [25]. Aclinical MRI or computed tomography (CT) scan was usedto determine the presence of cerebral small vessel diseaseDao et al. BMC Geriatrics  (2017) 17:133 Page 2 of 9which was based on the presence of periventricular anddeep white matter lesions and at least one lacunar infarctand the absence of non-lacunar territorial (cortical and/orcortico-subcortical) strokes or other specific causes ofwhite matter lesions (i.e. MS, leukodystrophies, sarcoidosis,brain irradiation). Mild cognitive impairment was definedas a Montreal Cognitive Assessment (MOCA) score < 26/30 at baseline [26]. SIVCI diagnosis also required evidenceof progressive cognitive decline (compared with previouslevel of cognitive function) as confirmed through medicalrecords or caregiver/family member interviews. Overall,participants were generally functioning independentlyand living in the community with minimal assistanceby family or caregiver.Study Inclusion Criteria: Both inclusion and exclusioncriteria have been published previously [24]. Briefly, indi-viduals were eligible for study entry if they met the fol-lowing criteria: 1) aged 55 years or older; 2) MOCAscore < 26/30 at screening [26]; 3) Mini-Mental StateExamination score (MMSE) ≥ 20 at screening [27]; 4) ifparticipants are on cognitive medications (e.g. donepezil,galantamine, rivastigmine, memantine, etc.) they mustbe on a stable and fixed dose that is not expected tochange during the 12-month study period, or, if they arenot on any of these medications, they are not expectedto start them during the 12-month study period; and 5)provide written informed consent. Exclusion criteriaincluded: 1) diagnosed with dementia of any type (e.g.Alzheimer’s disease, dementia with lewy bodies, frontal-temporal dementia) or other neurological conditions(e.g. multiple sclerosis, Parkinson’s disease); 2) takingmedications that may negatively affect cognitive func-tion; and 3) participation in a clinical drug trial concur-rent to this study.This analysis included a sub-set of 22 participants(exercise group n = 11; control group n = 11) who metthe overall study eligibility criteria and volunteered tocomplete a PET scan.Descriptive variablesAt baseline, we collected information regarding age, sex,body mass index, waist-hip ratio, and comorbid condi-tions were measured using the Functional ComorbidityIndex. In addition, we report WMH volume for a sub-sample of participants.Dependent variablesGlobal cognitive functionADAS-Cog: This test primarily measures memory, lan-guage, and praxis. There are 11 items and scores rangefrom 0 to 70 with higher scores indicating greater cogni-tive dysfunction [28].Executive functionsTrail Making Test (Part B minus A): This test primarilymeasures set shifting [29]. Participants were asked todraw lines connecting encircled numbers sequentially(Part A) and to alternate between numbers and letters(Part B). The difference in time to complete Part B andPart A was calculated; smaller difference indicated betterperformance.Verbal Digit Span Test (Forward minus Backward):This test primarily measures working memory [30]. Par-ticipants repeated progressively longer random numbersequences in the same order as presented (forward) andin the reversed order (backward). The difference in scorebetween the two tests was calculated; smaller differenceindicated better performance.Stroop Test: This test primarily measures selective at-tention and conflict resolution [31]. Participants com-pleted three conditions (80 trials each): 1) reading outcolor words printed in black ink; 2) reading out the dis-play color of colored-X’s; and 3) participants were showna page with color-words printed in incongruent coloredinks and were asked to name the ink color in which thewords were printed. The time difference between thethird condition and second condition was calculated;smaller difference indicated better performance.Information processing speedDigit Symbol Substitution Test (DSST): This test pri-marily measures information processing speed and psy-chomotor speed [29]. Participants were first presentedwith a legend of numbers (1 to 9) and their correspond-ing symbols. They were then presented with a series ofnumbers, organized in a pre-defined random order, andwere asked to fill in the corresponding symbol. Partici-pants were given 90 s to complete the task. A highernumber of correct answers in this time period indicatedbetter performance.Falls riskPhysiological Profile Assessment (PPA): This test as-sesses falls risk [32]. The PPA involves the following sub-scales: 1) proprioception; 2) edge contrast sensitivity; 3)quadriceps strength; 4) hand reaction time; and 5) pos-tural sway. Each item has a relative weighting and a sum-mary z-score is calculated that indicates: mild risk (0–1);moderate risk (1–2); high risk (2–3); and marked risk forfuture fall (3 and above) [32]. The PPA is a reliable [33]and valid [34, 35] measure of falls risk in older adults.Independent variableAmyloid-β imagingDetails of the Aβ imaging protocol have been publishedpreviously [12]. PET scans were performed using 11C–Pittsburgh Compound-B (PIB) produced at UBC TRIUMF.Dao et al. BMC Geriatrics  (2017) 17:133 Page 3 of 9Scans were performed in 3-D mode using the GE Advancetomograph (General Electric, Canada/USA). A 90-min dy-namic acquisition started at tracer injection and data wereframed into a 18 × 300 sec imaging sequence.Parametric images of the non-displaceable bindingpotential (BPND) [36] were generated using tissue inputLogan graphical analysis [37, 38] with the cerebellum asthe reference region [39, 40]. A mean PIB-PET imagewas created by averaging radiotracer concentration overthe entire scan duration – this image was used for co-registration and ROI definition purposes. Using SPM 8(Wellcome Department of Cognitive Neurology, Insti-tute of Neurology, University College London) each sub-ject’s T1-weighted MRI image was co-registered to thecorresponding mean PIB-PET image. Each subject’s MRIimage was then normalized to the SPM MNI305 tem-plate and the corresponding transformation parameterswere applied to the subject’s PET images (mean andparametric images). For those without MRI scans (5 sub-jects did not scan due to MR contraindications), thesubject’s mean PIB-PET image was normalized to anaverage PIB-PET image template of 6 healthy controlparticipants.Regions of interest (ROIs) analysis: A custom set ofROIs was defined on the coronal view of the MNI305template [41]. These ROIs were transposed to each sub-ject’s warped MRI and mean-PET images (in MNI space)and adjusted as necessary. The modified set of ROIs wasthen applied to the parametric PIB-PET image and theaverage PIB BPND within each ROI was extracted. GlobalPIB binding was determined by averaging values in bilat-eral frontal (combined orbitofrontal and medial prefrontalcortex), parietal (combined angular gyrus, superior par-ietal, precuneus, and supramarginal gyrus), temporal(combined lateral temporal and middle temporal gyrus),and occipital cortices, striatum (putamen and caudate nu-cleus), and anterior and posterior cingulate gyrus [10].Statistical analysisAll statistical analyses were performed using StatisticalPackage for the Social Sciences 22.0. We conducted ahierarchical multiple linear regression to determine theunique contribution of Aβ plaque deposition on 12-month change in cognitive function and falls risk. Wecontrolled for experimental group (i.e. aerobic exercisetraining or usual care control) and baseline score. Agewas initially included as a covariate but it did not signifi-cantly alter the results and was removed for a parsimoni-ous model. The dependent variable for all models waschange in the outcome of interest. Change in ADAS-Cog,Trail Making Test, Verbal Digit Span Test, Stroop Test,and PPA was calculated as baseline minus 12-monthscores. Change in DSST was calculated as12-month minusbaseline scores. In all instances, higher change scoresrepresent improved performance. We report adjusted R2values, which penalizes the explained variance for eachadditional covariate, resulting in a more realistic estimateof explained variance. For each hierarchical regressionmodel, we computed collinearity statistics (tolerance andvariance inflation factor), histograms of the residuals, andscatterplots of the predicted versus residual values to en-sure that the assumptions of linear regression were met.In all models, mutlicollinearity was not an issue amongpredictor variables, and the residuals were normally dis-tributed and homoscedastic. To correct for multiple com-parisons across all regression models, we applied theBenjamini-Hochberg [42] procedure to obtain a false dis-covery rate (FDR) corrected threshold using alpha = 0.05.ResultsDescriptive variablesThemean age was 72 ± 7.91 years (minimum age = 56 years;maximum age = 84 years), the average MOCA score was23.32 ± 2.08, and global PIB BPND was 0.10 ± 0.23. Six outof 22 participants did not complete an MRI scan – 5people had MR contraindications and 1 MRI scanwas discarded due to severe motion artifacts. Amongthe 16 participants with MRI data, WMH volumeranged from 76.38–10,058.89 mm3 with an average of2004.40 ± 2761.15 mm3. Compared with the participantsin the RCT that did not complete PIB scans, this subsetwas similar in age (mean difference = 3.80, p > 0.05), buthad a higher mean MOCA score (mean difference = 3.14,p ≤ 0.05). Detailed demographic characteristics and neuro-psychological test results are presented in Table 1.Global cognitive functionADAS-Cog: PIB BPND was not significantly associatedwith change in ADAS-Cog (p > 0.05).Executive functionsTrail Making Test (Part B minus A): Higher PIB BPNDwas significantly associated with decreased set shifting(β = −0.68, p < 0.01), the total adjusted variance accountedby the final model was 38.5% – Table 2.Verbal Digit Span Test (Forward minus Backward):PIB BPND was not significantly associated with changein working memory (p > 0.05).Stroop Test: Higher PIB BPND was significantly associ-ated with decreased selective attention and conflict reso-lution (β = −0.54, p = 0.01), the total adjusted varianceaccounted by the final model was 31.4% – Table 3.Information processing speedDSST: Higher PIB BPND was significantly associated withdecreased information processing speed (β = −0.56,p = 0.01), the total adjusted variance accounted by the finalmodel was 20.6% – Table 4.Dao et al. BMC Geriatrics  (2017) 17:133 Page 4 of 9Falls riskPPA: Increased PIB BPND was significantly associatedwith increased falls risk (β = −0.39, p = 0.03), the totaladjusted variance accounted by the final model was51.3% – Table 5.DiscussionCurrently, much of our knowledge on the effects of co-existing Aβ pathology in SIVCI is based on cross-sectionalstudies [11, 12, 22] and little is known about their impacton changes in cognitive function and mobility over time.Table 1 Descriptive CharacteristicsVariable Exercise groupn = 11Control groupn = 11TotalN = 22Mean SD Mean SD Mean SDAge 70.00 7.29 73.45 8.47 71.73 7.91Female sex, No. (%) 3 27.3 4 36.4 7 31.81MOCA 22.73 2.20 23.91 1.87 23.32 2.08Waist-to-hip ratio 0.90 0.08 0.93 0.08 0.91 0.08Body mass index 27.14 6.24 26.75 2.98 26.95 4.78FCI 3.09 1.81 3.55 1.69 3.32 1.73Total medications 3.27 3.10 5.00 3.90 4.14 3.55Beta-blockers, No. (%) 3 27 2 18 5 23Global PIB BPND 0.14 0.24 0.07 0.23 0.10 0.23WMH volume (mm3) 1277.82a 1446.90 2569.52b 3450.16 2004.40c 2761.15Baseline AssessmentsADAS-Cog 10.65 4.76 8.61 2.60 9.63 3.88TMT B-A, sec. 45.07 20.93 55.95 28.14 50.51 24.84VDST F-B, sec. 2.45 2.88 4.00 2.61 3.23 2.79Stroop CW-C, sec. 68.33 30.26 54.54 20.02 61.44 26.01DSST 26.27 8.01 25.27 5.97 25.77 6.91PPA 0.54 1.50 0.47 0.91 0.50 1.21Final AssessmentsADAS-Cog 9.57 4.70 6.86 2.77 8.22 4.01TMT B-A, sec. 76.43 69.32 66.45 51.88 71.44 59.97VDST F-B, sec. 2.18 1.83 3.27 1.85 2.73 1.88Stroop CW-C, sec. 66.41 25.91 55.60 32.37 61.00 29.14DSST 26.09 8.93 23.55 8.47 24.82 8.59PPA 0.12 1.23 0.54 0.59 0.33 0.96SD Standard Deviation, MOCA Montreal Cognitive Assessment (max. Score 30), FCI Functional comorbidity index (total number of comorbidities), ADAS-CogAlzheimer’s Disease Assessment Scale - Cognitive subscale (max. Score of 70), TMT B-A Trail Making Test, Part B minus Part A, VDST F-B Verbal Digit Span Test,Forward minus Backward, Stroop CW-W Stroop Color Words minus Stroop colored x’s, DSST Digit Symbol Substitution Test, PPA Physiological Profile Assessment;aExercise group - WMH volume, n = 7; bControl group - WMH volume, n = 9; cTotal - WMH volume, n = 16Table 2 Multiple regression model assessing the contribution of amyloid on change in Trail Making TestIndependent variables R2 Adjusted R2 R2 change Unstandardized B (Standard error) Standardized β P- valueStep 1 0.04 −0.07 0.04 0.70Group 20.16 (25.44) 0.18 0.44TMT baseline 0.07 (0.52) 0.03 0.90Step 2 0.47 0.39 0.44a <0.01Group 5.42 (19.70) 0.05 0.79TMT baseline 0.40 (0.41) 0.18 0.34PIB BPND −166.43 (43.09) −0.68 <0.01TMT Trail Making Test, Part B minus Part A baseline scoreaSignificant after FDR adjustmentDao et al. BMC Geriatrics  (2017) 17:133 Page 5 of 9We found that higher Aβ deposition was associated withgreater decrements in set shifting, attention and conflictresolution , and information processing speed over a 12-month period. In addition, we found that people withgreater Aβ deposition displayed increased falls risk atfollow-up. These results indicate that co-existing Aβplaque deposition may play a role in subsequent cognitiveand mobility declines in older adults with SIVCI.Previous studies assessing the role of Aβ on cognitivefunction in SIVCI have produced equivocal results. Onestudy found that Aβ was correlated with decreased per-formance on tests of immediate and delayed recall ofverbal learning but not executive functions [22]. Anotherstudy found that Aβ was independently associated withcognitive impairment in multiple domains, includinglanguage, visuospatial, memory, and executive functions[11]. A published cross-sectional analysis of this data setfound that increased Aβ plaque deposition was associ-ated with poorer performance in global cognitive func-tion as measured by ADAS-Cog [12]. In the currentanalysis, we did not find an association between Aβplaque deposition and change in global cognitive func-tion; however, we found that greater Aβ deposition wasassociated with declines in specific executive processesand information processing speed over 12-months.Our findings suggest that symptoms associated withSIVCI (i.e. executive dysfunction and decreased informationprocessing speed) are amplified by secondary Aβ pathology.Few studies have been conducted to assess the impact of Aβon change in cognitive function in people with a clinicaldiagnosis of SIVCI. The Amyloid PET Imaging for Subcor-tical Vascular Dementia (AMPETIS) study found that PIBpositivity was associated with faster declines in attention,visuospatial skills, visual memory, episodic memory, and ver-bal learning, but no significant declines in executive func-tions were detected [23]. However, we note that only asingle executive measure was included – phonemic andsemantic verbal fluency. Although verbal fluency tests do in-volve aspects of executive control, they do not isolate thethree main components of executive functions: set shifting,working memory/updating, and inhibition of dominant re-sponses [43] – which we targeted in the present study. Fur-thermore, we report that greater Aβ plaque deposition isassociated with reduced information processing speed overtime. In summary, these preliminary analyses indicated thatco-existing Aβ plaques may be detrimental to multiple do-mains of cognitive function in people with SIVCI.We also found that increased Aβ plaque deposition wasassociated with increased falls risk. This concurs withprevious literature – a study in healthy community-dwelling older adults found that higher Aβ was associatedwith faster time to first fall over 12-months [21]. Inaddition, epidemiological studies found that 42% of acommunity sample with mild to moderately severe AD fellTable 3 Multiple regression model assessing the contribution of amyloid on change in Stroop TestIndependent variables R2 Adjusted R2 R2 change Unstandardized B (Standard error) Standardized β P- valueStep 1 0.13 0.04 0.13 0.26Group 2.12 (11.12) 0.04 0.85Stroop baseline 0.37 (0.22) 0.38 0.11Step 2 0.41 0.31 0.28a 0.01Group −0.74 (9.46) −0.02 0.94Stroop baseline 0.45 (0.19) 0.46 0.03PIB BPND −59.67 (20.46) −0.54 0.01Stroop CW-W Stroop Color Words minus Stroop colored x’s baseline scoreaSignificant after FDR adjustmentTable 4 Multiple regression model assessing the contribution of amyloid on change in Digit Symbol Substitution TestIndependent variables R2 Adjusted R2 R2 change Unstandardized B (Standard error) Standardized β P- valueStep 1 0.02 −0.08 0.02 0.79Group −1.55 (2.26) −0.16 0.50DSST baseline −0.01 (0.17) −0.01 0.96Step 2 0.32 0.21 0.30a 0.01Group −2.45 (1.97) −0.25 0.23DSST baseline −0.09 (0.15) −0.12 0.56PIB BPND −12.31 (4.41) −0.56 0.01DSST Digit Symbol Substitution Test baseline scoreaSignificant after FDR adjustmentDao et al. BMC Geriatrics  (2017) 17:133 Page 6 of 9within a 12-month period [44, 45]. This is supportedby studies that have identified impaired static and dy-namic balance, mobility, and gait dysfunction in earlyAD [45], which may contribute to an increased riskof falling. Furthermore, there is a strong associationbetween cognition, particularly executive functions,with gait and balance. Older people with poor execu-tive control walk slower, have increased stride vari-ability, have poorer performance on complex mobilitytasks, and fall more often [46]. Executive dysfunctionmay impair planning, control, and execution of move-ments, and thus, can increase falls risk [47].Our findings are not without limitations. First, thisstudy was a secondary analysis of an exercise interven-tion trial and it is unclear how exercise may have influ-enced cognitive function and falls risk. To minimizeexercise effects, we statistically controlled for groupmembership. Second, our small sample size requires thatthese findings be confirmed in larger follow-up studies.Third, we did not control for the presence of other ADand SIVCI pathologies such as NFT, lacunes, or WMH.This is important to note, as NFT have been associatedwith cognitive outcomes in AD. Lacunes and WMHhave been associated with both executive dysfunctionsand falls risk. However, the presence of Aβ has also beenassociated with executive functions and falls risk inpeople with MCI and AD, indicating a unique contribu-tion of Aβ on cognitive and mobility declines. Within asubset of this data with WMH volume quantification,including WMH volume and age as covariates did notsignificantly alter the results. As such, it is plausiblethat Aβ plaque deposition may independently contrib-ute to changes in cognitive function and falls risk inSIVCI.ConclusionsThe results of this study suggest that cerebral Aβ plaquedeposition is associated with greater declines in both ex-ecutive functions and information processing speed, aswell as greater increases in falls risk among older adultswith a primary SIVCI diagnosis. However, more studieswith larger samples and longer follow-up are needed tofully elucidate the impact of co-existing Aβ on diseaseprogression in SIVCI. Future therapies for SIVCI mayneed to account for the potential presence and effect ofamyloid for the optimal care of those with SIVCI.AbbreviationsAD: Alzheimer’s disease; Aβ: Amyloid-beta; BPND: Non-displaceable bindingpotential; CT: Computed tomography; DSST: Digit Symbol Substitution Test;FDR: False discovery rate; MCI: Mild cognitive impairment; NFT: Neurofibrillarytangles; PET: Positron emission tomography; PIB: 11C Pittsburgh compound B;PPA: Physiological Profile Assessment; RCT: Randomized controlled trial;ROI: Regions of interest; SIVCI: Subcortical ischemic vascular cognitiveimpairment; WMH: White matter hyperintensitiesAcknowledgmentsUBC TRIUMF is gratefully acknowledged for PET tracer production.FundingThis study was jointly funded by the Canadian Stroke Network, Heart andStroke Foundation of Canada, and Jack Brown & Family Alzheimer’s ResearchFoundation.Availability of data and materialsThe datasets used and/or analysed during the current study are availablefrom the corresponding author on reasonable request.Authors’ contributionsAll authors (ED, JRB, GYRH, VS, CJ, RT and TLA) contributed to study design,statistical analysis, data interpretation, and manuscript preparation. The finalversion of this manuscript was approved by all authors.Competing interestsThe authors declare that they have no competing interests.Consent for publicationNot applicable.Ethics approval and consent to participateEthical approval was obtained from the Vancouver Coastal Health ResearchInstitute (V07–01160) and the University of British Columbia’s ClinicalResearch Ethics Board (H07–01160). All subjects gave written informedconsent in accordance with the Declaration of Helsinki.Publisher’s NoteSpringer Nature remains neutral with regard to jurisdictional claims in publishedmaps and institutional affiliations.Table 5 Multiple regression model assessing the contribution of amyloid on change in Physiological Profile AssessmentIndependent variables R2 Adjusted R2 R2 change Unstandardized B (Standard error) Standardized β P- valueStep 1 0.45 0.39 0.45 <0.01Group −0.45 (0.32) −0.24 0.17PPA baseline 0.49 (0.13) 0.62 <0.01Step 2 0.58 0.51 0.13a 0.03Group −0.56 (0.29) −0.30 0.07PPA baseline 0.39 (0.13) 0.50 0.01PIB BPND −1.60 (0.67) −0.39 0.03PPA Physiological Profile Assessment baseline scoreaSignificant after FDR adjustmentDao et al. BMC Geriatrics  (2017) 17:133 Page 7 of 9Author details1Department of Physical Therapy, University of British Columbia, 212 – 2177Wesbrook Mall, Vancouver, BC V6T 1Z3, Canada. 2Djavad MowafaghianCentre for Brain Health, University of British Columbia, 2215 Wesbrook Mall,Vancouver, BC V6S 0A9, Canada. 3Department of Medicine, University ofBritish Columbia, UBC Hospital S152, 2211 Wesbrook Mall, Vancouver, BC V6T2B5, Canada. 4Department of Physics and Astronomy, University of BritishColumbia, 6224 Agricultural Road, Vancouver, BC V6T 1Z1, Canada. 5Schoolof Graduate Psychology, Pacific University, 190 SE 8th Avenue, Hillsboro, OR97123, USA. 6MS/MRI Research Group, University of British Columbia, 2215Wesbrook Mall, Vancouver, BC V6S 0A9, Canada. 7Centre for Hip Health andMobility, Robert H.N. Ho Research Centre, 2635 Laurel Street, Vancouver, BCV5Z 1M9, Canada.Received: 4 March 2017 Accepted: 14 June 2017References1. Jellinger KA, Attems J. Prevalence of dementia disorders in the oldest-old:an autopsy study. Acta Neuropathol. 2010;119(4):421–33.2. Schneider JA, Arvanitakis Z, Bang W, Bennett DA. Mixed brain pathologiesaccount for most dementia cases in community-dwelling older persons.Neurology. 2007;69(24):2197–204.3. Mattson MP. Pathways towards and away from Alzheimer's disease. Nature.2004;430(7000):631–9.4. Rockwood K, Macknight C, Wentzel C, Black S, Bouchard R, Gauthier S, et al.The diagnosis of "mixed" dementia in the consortium for the investigation ofvascular impairment of cognition (CIVIC). Ann N Y Acad Sci. 2000;903:522–8.5. Esiri MM, Nagy Z, Smith MZ, Barnetson L, Smith AD. Cerebrovascular diseaseand threshold for dementia in the early stages of Alzheimer's disease.Lancet. 1999;354(9182):919–20.6. Nagy Z, Esiri MM, Jobst KA, Morris JH, King EM, McDonald B, et al. Theeffects of additional pathology on the cognitive deficit in Alzheimer disease.J Neuropathol Exp Neurol. 1997;56(2):165–70.7. Kalaria RN. The role of cerebral ischemia in Alzheimer's disease. NeurobiolAging. 2000;21(2):321–30.8. Iadecola C. The overlap between neurodegenerative and vascular factors inthe pathogenesis of dementia. Acta Neuropathol. 2010;120(3):287–96.9. Marnane M, Hsiung GY. Could better phenotyping small vessel diseaseprovide new insights into Alzheimer disease and improve clinical trialoutcomes? Curr Alzheimer Res. 2016;13(7):750–63.10. Park JH, Seo SW, Kim C, Kim SH, Kim GH, Kim ST, et al. Effects ofcerebrovascular disease and amyloid beta burden on cognition in subjectswith subcortical vascular cognitive impairment. Neurobiol Aging. 2014;35(1):254–60.11. Lee MJ, Seo SW, Na DL, Kim C, Park JH, Kim GH, et al. Synergistic effects ofischemia and beta-amyloid burden on cognitive decline in patientswith subcortical vascular mild cognitive impairment. JAMA psychiatry.2014;71(4):412–22.12. Dao E, Hsiung GY, Sossi V, Jacova C, Tam R, Dinelle K, et al. Exploring theeffects of coexisting amyloid in subcortical vascular cognitive impairment.BMC Neurol. 2015;15:197.13. Pike KE, Savage G, Villemagne VL, Ng S, Moss SA, Maruff P, et al. Beta-amyloid imaging and memory in non-demented individuals: evidence forpreclinical Alzheimer's disease. Brain : a journal of neurology. 2007;130(Pt11):2837–44.14. Rosenberg PB, Wong DF, Edell SL, Ross JS, Joshi AD, Brašić JR, et al. Cognitionand amyloid load in Alzheimer disease imaged with florbetapir F 18(AV-45)positron emission tomography. Am J Geriatr Psychiatry. 2013;21(3):272–8.15. Weintraub S, Wicklund AH, Salmon DP. The neuropsychological profile ofAlzheimer disease. Cold Spring Harbor perspectives in medicine.2012;2(4):a006171.16. Lim YY, Maruff P, Pietrzak RH, Ames D, Ellis KA, Harrington K, et al. Effect ofamyloid on memory and non-memory decline from preclinical to clinicalAlzheimer’s disease. Brain : a journal of neurology. 2014;137(1):221–31.17. Small GW, Siddarth P, Kepe V, et al. Prediction of cognitive decline bypositron emission tomography of brain amyloid and tau. Arch Neurol.2012;69(2):215–22.18. Del Campo N, Payoux P, Djilali A, Delrieu J, Hoogendijk EO, Rolland Y, et al.Relationship of regional brain beta-amyloid to gait speed. Neurology.2016;86(1):36–43.19. Nadkarni NK, Perera S, Snitz BE, Mathis CA, Price J, Williamson JD, et al.Association of brain amyloid-beta with slow gait in elderly individualswithout dementia: influence of cognition and apolipoprotein E epsilon4genotype. JAMA neurology. 2017;74(1):82–90.20. Wennberg AMV, Savica R, Hagen CE, Roberts RO, Knopman DS, Hollman JH,Vemuri P, Jack CR, Petersen RC, Mielke MM. Cerebral Amyloid Deposition IsAssociated with Gait Parameters in the Mayo Clinic Study of Aging. J AmGeriatr Soc. 2017;65(4):792–9.21. Stark SL, Roe CM, Grant EA, Hollingsworth H, Benzinger TL, Fagan AM, et al.Preclinical Alzheimer disease and risk of falls. Neurology. 2013;81(5):437–43.22. Lee JH, Kim SH, Kim GH, Seo SW, Park HK, Oh SJ, et al. Identification of puresubcortical vascular dementia using 11C-Pittsburgh compound B.Neurology. 2011;77(1):18–25.23. Ye BS, Seo SW, Kim JH, Kim GH, Cho H, Noh Y, et al. Effects of amyloid andvascular markers on cognitive decline in subcortical vascular dementia.Neurology. 2015;85(19):1687–93.24. Liu-Ambrose T, Eng JJ, Boyd LA, Jacova C, Davis JC, Bryan S, et al.Promotion of the mind through exercise (PROMoTE): a proof-of-conceptrandomized controlled trial of aerobic exercise training in older adults withvascular cognitive impairment. BMC Neurol. 2010;10:14.25. Hachinski V, Iadecola C, Petersen RC, Breteler MM, Nyenhuis DL, Black SE,et al. National Institute of Neurological Disorders and Stroke-CanadianStroke network vascular cognitive impairment harmonization standards.Stroke. 2006;37(9):2220–41.26. Nasreddine ZS, Phillips NA, Bedirian V, Charbonneau S, Whitehead V, Collin I,et al. The Montreal cognitive Assessment, MoCA: a brief screening tool formild cognitive impairment. J Am Geriatr Soc. 2005;53(4):695–9.27. Folstein MF, Folstein SE, McHugh PR. "Mini-mental state": A practicalmethod for grading the cognitive state of patients for the clinician. JPsychiatr Res. 1975;12(3):189–98.28. Kirk A. Target symptoms and outcome measures: cognition. Canad J NeurolSci. 2007;34(Suppl 1):S42–6.29. Spreen O, Strauss E. A compendium of neuropsychological tests:Administration, norms and commentary. 2nd ed. New York: OxfordUniversity Press; 1998.30. Wechsler D. Wechsler Adult Intelligence Scale—Revised. New York: ThePsychological Corporation, Harcourt Brace Jovanovich; 1981.31. Graf P, Uttl B, Tuokko H. Color- and picture-word Stroop tests: Performancechanges in old age. J Clin Exp Neuropsychol. 1995;17(3):390–415.32. Lord SR, Menz HB, Tiedemann A. A physiological profile approach to fallsrisk assessment and prevention. Phys Ther. 2003;83(3):237–52.33. Lord SR, Castell S. Physical activity program for older persons: effect onbalance, strength, neuromuscular control, and reaction time. Arch Phys MedRehabil. 1994;75(6):648–52.34. Lord SR, Clark RD, Webster IW. Physiological factors associated with falls inan elderly population. J Am Geriatr Soc. 1991;39(12):1194–200.35. Lord SR, Ward JA, Williams P, Anstey KJ. Physiological factors associated with fallsin older community-dwelling women. J Am Geriatr Soc. 1994;42(10):1110–7.36. Innis RB, Cunningham VJ, Delforge J, Fujita M, Gjedde A, Gunn RN, et al.Consensus nomenclature for in vivo imaging of reversibly binding radioligands.Journal of cerebral blood flow and metabolism : official journal of theInternational Society of Cerebral Blood Flow and Metabolism.2007;27(9):1533–9.37. Logan J, Fowler JS, Volkow ND, Wang GJ, Ding YS, Alexoff DL. Distributionvolume ratios without blood sampling from graphical analysis of PET data.Journal of cerebral blood flow and metabolism : official journal of theInternational Society of Cerebral Blood Flow and Metabolism. 1996;16(5):834–40.38. Logan J, Fowler JS, Volkow ND, Wolf AP, Dewey SL, Schlyer DJ, et al.Graphical analysis of reversible radioligand binding from time-activitymeasurements applied to [N-11C-methyl]-(−)-cocaine PET studies in humansubjects. Journal of cerebral blood flow and metabolism : official journal ofthe International Society of Cerebral Blood Flow and Metabolism. 1990;10(5):740–7.39. Lopresti BJ, Klunk WE, Mathis CA, Hoge JA, Ziolko SK, Lu X, et al. Simplifiedquantification of Pittsburgh compound B amyloid imaging PET studies: acomparative analysis. Journal of nuclear medicine : official publication,Society of Nuclear Medicine. 2005;46(12):1959–72.40. Price JC, Klunk WE, Lopresti BJ, Lu X, Hoge JA, Ziolko SK, et al. Kineticmodeling of amyloid binding in humans using PET imaging and Pittsburghcompound-B. Journal of cerebral blood flow and metabolism : officialDao et al. BMC Geriatrics  (2017) 17:133 Page 8 of 9journal of the International Society of Cerebral Blood Flow and Metabolism.2005;25(11):1528–47.41. Collins DL, Neelin P, Peters TM, Evans AC. Automatic 3D intersubject registrationof MR volumetric data in standardized Talairach space. J Comput Assist Tomogr.1994;18(2):192–205.42. Benjamini Y, Hochberg Y. Controlling the false discovery rate: a practicaland powerful approach to multiple testing. J R Stat Soc Ser B Methodol.1995;57(1):289–300.43. Miyake A, Friedman NP, Emerson MJ, Witzki AH, Howerter A, Wager TD. Theunity and diversity of executive functions and their contributions to complex"frontal lobe" tasks: a latent variable analysis. Cogn Psychol. 2000;41(1):49–100.44. Horikawa E, Matsui T, Arai H, Seki T, Iwasaki K, Sasaki H. Risk of falls inAlzheimer's disease: a prospective study. Intern Med. 2005;44(7):717–21.45. Suttanon P, Hill KD, Said CM, Logiudice D, Lautenschlager NT, Dodd KJ.Balance and mobility dysfunction and falls risk in older people with mild tomoderate Alzheimer disease. American journal of physical medicine &rehabilitation / Association of Academic Physiatrists. 2012;91(1):12–23.46. van Iersel MB, Kessels RP, Bloem BR, Verbeek AL, Olde Rikkert MG. Executivefunctions are associated with gait and balance in community-living elderlypeople. J Gerontol Ser A Biol Med Sci. 2008;63(12):1344–9.47. Liu-Ambrose T, Nagamatsu LS, Hsu CL, Bolandzadeh N. Emerging concept:'central benefit model' of exercise in falls prevention. 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